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Growth Metrics and Key Performance Indicators Questions

Comprehensive knowledge of growth metrics and key performance indicators used to measure user acquisition, engagement, retention, and revenue. Candidates should understand definitions, business meaning, and how to calculate metrics from raw event and transaction data. Core metrics include customer acquisition cost, lifetime value, lifetime value to customer acquisition cost ratio, conversion rate, churn rate, retention rate, monthly active users, daily active users, cohort retention, activation, engagement, average revenue per user, payback period, viral coefficient, and growth rate over time. Candidates should be able to choose appropriate leading and lagging indicators, explain unit economics, and reason about tradeoffs across acquisition, activation, retention, revenue, and referral stages. Practical skills include designing instrumentation and tracking for events and transactions, selecting attribution windows, avoiding sampling and attribution pitfalls, cleaning and deduplicating event streams, and calculating metrics by cohort and segment. Candidates must be able to perform funnel analysis and cohort analysis to diagnose problems, prioritize optimization levers, set metric baselines and success criteria for controlled experiments and split tests, assess sensitivity to seasonality pricing changes and growth initiatives, and communicate metric driven recommendations and dashboards to stakeholders. They should also identify which metrics matter for different business models such as business to business versus business to consumer and subscription versus transactional models.

HardTechnical
0 practiced
You're running an A/B test on activation with baseline conversion 20% and you want to detect an absolute lift of 1.5 percentage points. Explain how to compute required sample size and test duration given daily traffic of 200k users. Discuss adjustments needed for multiple metrics, sequential monitoring, and non-equal allocation ratios.
HardSystem Design
0 practiced
Design an anomaly detection system for core KPIs (DAU, activation, revenue) that minimizes false positives while detecting regressions quickly. Choose detection algorithms, baseline modeling, thresholds, methods to handle holidays/seasonality, alerting cadence, and how to triage alerts.
HardTechnical
0 practiced
Build a cohort-based forecasting approach for monthly recurring revenue (MRR) for the next 12 months. Detail required inputs (cohort sizes, conversion, retention decay, ARPU), the forecasting math, how to include seasonality and planned product changes, and how to present optimistic/base/pessimistic scenarios with assumptions.
MediumTechnical
0 practiced
Compare last-click, first-click, linear multi-touch, and algorithmic multi-touch attribution. For a growth team with limited data, which approach would you recommend initially and why? Propose a pragmatic migration path toward multi-touch attribution when more data becomes available.
MediumTechnical
0 practiced
Compute the viral coefficient given: each user sends on average 0.8 invites and 12% of invites convert to active users who then behave the same. Calculate the viral coefficient and explain whether viral growth will be sustained.

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